Optimizing a hybrid vendor-managed inventory and transportation problem with fuzzy demand: An improved particle swarm optimization algorithm

Sadeghi, Javad; Sadeghi, Saeid; Niaki, Seyed Taghi Akhavan

doi:10.1016/j.ins.2014.02.075

Cited by 115 publications

(55 citation statements)

References 43 publications

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“…This paper considers the optimization of multiple objectives such as minimizing costs, total earliness and tardiness, and total deteriorated items during transportation in distribution centers. Sadeghi et al [25] developed a VMI model in a multi-retailor single-vendor SCM which aims to nd the optimal retailers' order quantities so that the total inventory and transportation costs can be minimized while several constraints are satis ed.…”

Section: Literature Reviewmentioning

confidence: 99%

A fuzzy dynamic multi-objective, multi-item model by considering customer satisfaction in supply chain

et al. 2017

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Abstract. Customer satisfaction is an important issue in competitive strategic management of companies. Logistical and cross-functional drivers of supply chain have an important role in managing customer satisfaction. Customer satisfaction depends on quality, cost, and delivery. In this paper, a fuzzy mixed integer nonlinear programming model is proposed for a multi-item multi-period problem in a multi-level supply chain. Minimization of costs, manufacturing and transportation time, transportation risks, maximization of quality by minimizing the number of defective products, and maximization of customers' service levels are considered to be objective functions of the model. Furthermore, it is assumed that the demand rates are fuzzy values. An exact "-constraint approach is used to solve the problem. The problem is computationally intractable. Therefore, the Non-dominant Sorting Genetic Algorithm (NSGA-II) is developed to solve it. The Taguchi method is utilized to tune the NSGA-II parameters. Finally, some numerical examples are generated and solved to evaluate the performance of the proposed model and solving methods.

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Section: Literature Reviewmentioning

confidence: 99%

A fuzzy dynamic multi-objective, multi-item model by considering customer satisfaction in supply chain

et al. 2017

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“…Many studies have been made to improve modified particle swam optimization (MPSO) algorithm in continuous optimization (cf. Pedrycz et al [24], Sadeghi et al [25], Koulinas et al [26]). …”

Section: Introductionmentioning

confidence: 98%

Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

Jana

Pramanik

Maiti

2016

Neural Comput & Applic

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The transportation problem (TP) is an important supply chain optimization problem in the traffic engineering. This paper maximizes the total profit over a three-tiered distribution system consisting of plants, distribution centers (DCs) and customers. Plants produce multiple products that are shipped to DCs. If a DC is used, then a fixed cost (FC) is charged. The customers are supplied by a single DC. To characterize the uncertainty in the practical decision environment, this paper considers the unit cost of TP, FC, the supply capacities and demands as Gaussian type-2 fuzzy variables. To give a modeling framework for optimization problems with multifold uncertainty, different reduction methods were proposed to transform a Gaussian type-2 fuzzy variable into a type-1 fuzzy variable by mean reduction method and CV reduction method. Then, the TP was reformulated as a chanceconstrained programming model enlightened by the credibility optimization methods. The deterministic models are then solved using two different soft computing techniques-generalized reduced gradient and modified particle swarm optimization, where the position of each particle is adjusted according to its own experience and that of its neighbors. The numerical experiments illustrated the application and effectiveness of the proposed approaches.

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“…Some recent works in this context are Sakawa [15], who applied fuzzy goal programming method for solving multi-objective nonlinear programming problem; Ojha et al [16] presented a STP for item with fixed charge, vehicle cost and price discounted varying charge using genetic algorithm; Fegad et al [17] used interval and triangular membership functions in a TP. A few recent works in the field of TP and STP are that of Zavardehi et al [18], Figueroa-Garc and Hernandez [19], Kaur and Kumar [20], Jana et al [21], Tao and Xu [22], Kundu et al [23], Sadeghi et al [24], Liu et al [25] etc. Yang et al [26] applied methods of reduction for type-2 fuzzy variables to STP.…”

Section: Introductionmentioning

confidence: 99%

Expectations of the reductions for type-2 trapezoidal fuzzy variables and its application to a multi-objective solid transportation problem via goal programming technique

Dutta¹,

Jana

2017

J. Uncertain. Anal. Appl.

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In this paper, based on the concepts of credibility measure and expectation theory, we derive the expectation formulae for the three reductions of a type-2 trapezoidal fuzzy variable (T2TrFV), which are attained by adopting the critical value (CV) reduction methods. We minimize the total transportation cost and the total transportation time over a single layered distribution system consisting of vendors and customers represented as a multi-objective solid transportationproblem. To portray the uncertainty in a real life choice environment, we consider the unit cost of transportation, demands, availabilities, conveyance capacities, unit transportation time and unit loading and unloading time as T2TrFVs. The corresponding deterministic model, which is obtained by the application of expectation formulas deduced earlier, is converted to a single objective optimization problem using goal programming technique and weighted sum method via the soft computing technique-generalized reduced gradient (LINGO-14.0). A numerical experiment is finally illustrated and corresponding graphical representations are provided.

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Optimizing a hybrid vendor-managed inventory and transportation problem with fuzzy demand: An improved particle swarm optimization algorithm

Cited by 115 publications

References 43 publications

A fuzzy dynamic multi-objective, multi-item model by considering customer satisfaction in supply chain

A fuzzy dynamic multi-objective, multi-item model by considering customer satisfaction in supply chain

Mean and CV reduction methods on Gaussian type-2 fuzzy set and its application to a multilevel profit transportation problem in a two-stage supply chain network

Expectations of the reductions for type-2 trapezoidal fuzzy variables and its application to a multi-objective solid transportation problem via goal programming technique

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